针对SST和QNLI两个数据集的概率分布通过电影评价分别计算在SST和QNLI概率分布下的熵率并分析所计算的熵率结果PROBLEM 5 – Entropy Rate 20 pts Write a python function that computes the per-word entropy rate of a message relative to a specific probabilit

To compute the per-word entropy rate of a message relative to a specific probability distribution, you can follow these steps:

1. Import the necessary libraries:

import math
from collections import Counter

2. Define a function to compute the entropy rate:

def entropy_rate(message, distribution):
    word_counts = Counter(message.split())
    total_words = sum(word_counts.values())
    
    entropy = 0
    for word, count in word_counts.items():
        probability = count / total_words
        entropy += probability * math.log2(distribution[word])
        
    entropy_rate = -entropy / total_words
    return entropy_rate

3. Define the probability distributions for SST and QNLI datasets. You can obtain these distributions by training language models on the respective datasets or by using pre-trained models. For simplicity, let's assume we already have these distributions as dictionaries:

sst_distribution = {"good": 0.4, "bad": 0.6, "great": 0.3, "awful": 0.7}
qnli_distribution = {"true": 0.5, "false": 0.5, "correct": 0.3, "incorrect": 0.7}

4. Find a recent movie review online and store it in a variable:

review = "This movie is really good. The acting was great and the story was captivating."

5. Compute the entropy rates of the movie review using the distributions for SST and QNLI datasets:

sst_entropy_rate = entropy_rate(review, sst_distribution)
qnli_entropy_rate = entropy_rate(review, qnli_distribution)

6. Print the results:

print("Entropy rate with SST distribution:", sst_entropy_rate)
print("Entropy rate with QNLI distribution:", qnli_entropy_rate)

The entropy rate measures the average uncertainty or randomness of the words in the message relative to the given probability distribution. A higher entropy rate indicates a higher level of uncertainty or diversity in word usage. In the context of movie reviews, a higher entropy rate suggests a more varied or ambiguous sentiment expressed in the review. Conversely, a lower entropy rate implies a more predictable or consistent sentiment. By comparing the entropy rates computed using different distributions (SST and QNLI), we can analyze how the sentiment expressed in the movie review aligns with the sentiment distributions of the two datasets.